Southampton: Mapping Energy Poverty & Deprivation
Ben Anderson
Last run at: 2021-07-26 14:46:40
1 Intro
Mapping fuel poverty, deprivation & doemstic energy use in Southampton to focus on areas which are high fp & high deprivation.
Based on:
- Meghan Kinglsey-Walsh’s MSc project code
- Tom Rushby’s open geography example.
2 Mapping Domestic Electricity consumption (LSOAs)
##
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## between, first, last##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `Middle Layer Super Output Area (MSOA) Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `Lower Layer Super Output Area (LSOA) Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Total number of domestic electricity meters` = col_double(),
## `Total domestic electricity consumption (kWh)` = col_double(),
## `Mean domestic electricity consumption
## (kWh per meter)` = col_double(),
## `Median domestic electricity consumption
## (kWh per meter)` = col_double()
## )## Loading LSOA boundaries from file##
## Basingstoke and Deane East Hampshire Eastleigh
## 109 72 77
## Fareham Gosport Hart
## 73 53 57
## Havant Isle of Wight New Forest
## 78 89 114
## Portsmouth Southampton Test Valley
## 125 148 71
## Winchester
## 70Focus on Southampton
3 Mapping Domestic Gas (LSOAs)
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `Local Authority Name` = col_character(),
## `Local Authority Code` = col_character(),
## `MSOA Name` = col_character(),
## `Middle Layer Super Output Area (MSOA) Code` = col_character(),
## `LSOA Name` = col_character(),
## `Lower Layer Super Output Area (LSOA) Code` = col_character(),
## `Number of consuming meters` = col_double(),
## `Consumption (kWh)` = col_double(),
## `Mean consumption (kWh per meter)` = col_double(),
## `Median consumption (kWh per meter)` = col_double(),
## `Number of non-consuming meters` = col_character()
## )
Some areas are very high gas…
3.0.1 Correlating gas & elecricity
4 Mapping the Indices of Multiple Deprivation (LSOAs)
2019 data
Should be a negative correlation with gas & electricity
##
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## lsoa11cd = col_character(),
## lsoa11nm = col_character(),
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## LSOA01NM = col_character(),
## LADcd = col_character(),
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## )
## ℹ Use `spec()` for the full column specifications.
Check correlations with energy
Much stronger relationship between mean gas use & IMD score in Southampton
5 Mapping Fuel Poverty (LSOAs)
2019 Fuel Poverty
## Warning: Missing column names filled in: 'X9' [9]##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## `LSOA Code` = col_character(),
## `LSOA Name` = col_character(),
## `LA Code` = col_character(),
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## Region = col_character(),
## `Number of households1` = col_number(),
## `Number of households in fuel poverty1` = col_double(),
## `Proportion of households fuel poor (%)` = col_double(),
## X9 = col_logical()
## )## Warning: 1 parsing failure.
## row col expected actual file
## 32845 Proportion of households fuel poor (%) a double England '/Users/ben/University of Southampton/HCC Energy Landscape Mapping project - Documents/General/data/Fuel Poverty/2019 LSOA_Fuel_Poverty.csv'
6 Correlating energy use & fuel poverty/deprivation
Figure 6.1 shows correlation of fuel poverty & IMD & energy
Figure 6.1: Correlation of IMD score, % in fuel poverty and mean energy demand (Southampton LSOAs)
IMD & % in fuel poverty correlate. Less clear correlation with mean energy. Need to check definitions… Also we might expect more of a correlation with the Income score (although INC score seems to drive IMD score in Southampton so…)
7 Selecting areas with high fuel poverty & high deprivation
Create a local quantile for each - where are the places with higest deprivation & fuel poverty?
## IMD_quinSoton pcFP_quinSoton n
## 1: <NA> <NA> 1
## 2: (5.75,16.5] <NA> 1
## 3: (5.75,16.5] (4,8] 27
## 4: (5.75,16.5] (8,10] 2
## 5: (5.75,16.5] (10,12] 3
## 6: (5.75,16.5] (12,25] 3
## 7: (16.5,25.1] (4,8] 11
## 8: (16.5,25.1] (8,10] 12
## 9: (16.5,25.1] (10,12] 4
## 10: (16.5,25.1] (12,25] 10
## 11: (25.1,36.1] (4,8] 6
## 12: (25.1,36.1] (8,10] 13
## 13: (25.1,36.1] (10,12] 6
## 14: (25.1,36.1] (12,25] 12
## 15: (36.1,67.2] (4,8] 1
## 16: (36.1,67.2] (8,10] 7
## 17: (36.1,67.2] (10,12] 18
## 18: (36.1,67.2] (12,25] 11Strange - why are there NAs?
Select them…
## LSOA11CD IMDScore IMDDec0 IMD_quinSoton pcFP pcFP_quinSoton
## 1: E01017155 45.775 1 (36.1,67.2] 25 (12,25]
## 2: E01017156 38.486 2 (36.1,67.2] 22 (12,25]
## 3: E01017153 42.322 2 (36.1,67.2] 21 (12,25]
## 4: E01017188 42.580 2 (36.1,67.2] 20 (12,25]
## 5: E01032750 38.842 2 (36.1,67.2] 19 (12,25]
## 6: E01017218 43.096 2 (36.1,67.2] 16 (12,25]
## 7: E01017210 53.919 1 (36.1,67.2] 14 (12,25]
## 8: E01017276 36.254 2 (36.1,67.2] 14 (12,25]
## 9: E01017154 51.852 1 (36.1,67.2] 13 (12,25]
## 10: E01017216 40.447 2 (36.1,67.2] 13 (12,25]
## 11: E01017237 50.152 1 (36.1,67.2] 13 (12,25]## Which national IMD decile are they in & what is mean % FP?## IMDDec0 mean_pcFP mean_IMDScore nLSOAs
## 1: 1 16.25000 50.42450 4
## 2: 2 17.85714 40.28957 7Map them
##
## Attaching package: 'plotly'## The following object is masked from 'package:raster':
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## layout## Warning: Ignoring unknown aesthetics: labelNote that some areas are high IMD but relatively low % fuel poverty
Figure 7.1 shows a leaflet version for pretty background to identify locations. When selected the LSOAs are coloured by their IMD Score.
## Coordinate Reference System:
## User input: OSGB 1936 / British National Grid
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## SCOPE["Engineering survey, topographic mapping."],
## AREA["United Kingdom (UK) - offshore to boundary of UKCS within 49°45'N to 61°N and 9°W to 2°E; onshore Great Britain (England, Wales and Scotland). Isle of Man onshore."],
## BBOX[49.75,-9,61.01,2.01]],
## ID["EPSG",27700]]Figure 7.1: Location of filtered LSOAs (coloured by IMD score)
8 The end
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